800 research outputs found

    Contextual anomaly detection in crowded surveillance scenes

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    AbstractThis work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour

    Event detection in pedestrian detection and tracking applications

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    In this paper, we present a system framework for event detection in pedestrian and tracking applications. The system is built upon a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes. Upon this framework we propose a pedestrian indexing scheme and suite of tools for detecting events or retrieving data from a given scenario

    A Recent Trend in Individual Counting Approach Using Deep Network

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    In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results

    Robust pedestrian detection and tracking in crowded scenes

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    In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

    A Survey on Counting People with Low Level Features

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    The main objective of this paper is to evaluate recent development in counting people with low level features. This paper describe the various techniques of counting people with low level features, compares them with the help of evaluation performance measures which are widely used for counting. The aim of this paper is to find the best method among some prominent exiting methods

    People counting and human detection in a challenging situation

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    Reliable people counting and human detection is an important problem in visual surveillance. In recent years, the field has seen many advances, but the solutions have restrictions: people must be moving, the background must be simple, and the image resolution must be high. This paper aims to develop an effective method for estimating the number of people and locate each individual in a low resolution image with complicated scenes. The contribution of this paper is threefold. First, postprocessing steps are performed on background subtraction results to estimate the number of people in a complicated scene, which includes people who are moving only slightly. Second, an Expectation Maximization (EM)-based method has been developed to locate individuals in a low resolution scene. In this method, a new cluster model is used to represent each person in the scene. The method does not require a very accurate foreground contour. Third, the number of people is used as a priori for locating individuals based on feature points. Hence, the methods for estimating the number of people and for locating individuals are connected. The developed methods have been validated based on a 4-hour video, with the number of people in the scene ranging from 36 to 222. The best result for estimating the number of people has an average error of 10% over 51 test cases. Based on the estimated number of people, some results of the EM-based method have also been shown. © 2006 IEEE.published_or_final_versio
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